Lead Data Engineer
New
T
TrolleyFintech payments
Remote within Quebec, Ontario, or Nova Scotia, CanadaFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 7+ years in data engineering
- Required Skills
- PythonSQLData engineeringDatabricks
Requirements
- Have 7+ years of data engineering experience, including ownership of a production data platform.
- Have experience managing, mentoring, or leading a small team of engineers day to day.
- Have deep experience with Databricks or a similar lakehouse, SQL, and Python.
- Have experience building reliable pipelines from operational databases and SaaS systems.
- Have experience supporting finance or accounting processes with data.
- Have hands-on experience using AI tools in engineering work and sound judgment about where AI is useful.
- Be comfortable working autonomously as the company's most senior data specialist.
- Preferred: formal people management experience and interest in growing a team.
- Preferred: experience in fintech, payments, or other regulated environments.
- Preferred: experience with customer-facing data products, embedded analytics, or data sharing.
- Preferred: experience building context layers, retrieval, or data pipelines for AI agents.
- Preferred: practical data governance and privacy experience, including PII handling, access control, and GDPR.
Responsibilities
- Manage, coach, and develop the data engineer on the team, with regular one-to-ones and clear goals.
- Set standards for data code review, testing, documentation, and on-call.
- Plan the team's work with Engineering, Finance, and Product, and coordinate as priorities shift.
- Own the Databricks lakehouse, including ingestion, modelling, data quality, and cost.
- Build and run pipelines from the production platform, Salesforce, NetSuite, and third-party sources.
- Maintain accurate, fast, self-serve internal reporting in Sigma, and make pipelines observable and recoverable.
- Run data processes for month-end close, revenue calculations, and vendor cost reconciliation; support audits and year-end reconciliation.
- Build and improve customer-facing data products, including the data connector and embedded reporting.
- Build data foundations for AI tools and agents, and identify opportunities to apply data and AI to the product and team workflows.
- Apply data management and privacy policies, and partner with InfoSec and Compliance on access, retention, and audit requirements.
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